Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#257831] of David B. Dunson


Papers Published

  1. Minsker, S; Srivastava, S; Lin, L; Dunson, DB, Scalable and robust Bayesian inference via the median posterior, 31st International Conference on Machine Learning, Icml 2014, vol. 5 (January, 2014), pp. 3629-3639, ISBN 9781634393973
    (last updated on 2019/05/19)

    Copyright 2014 by the author(s). Many Bayesian learning methods for massive data benefit from working with small subsets of observations. In particular, significant progress has been made in scalable Bayesian learning via stochastic approximation. However, Bayesian learning methods in distributed computing environments are often problem- or distribution-specific and use ad hoc techniques. We propose a novel general approach to Bayesian inference that is scalable and robust to corruption in the data. Our technique is based on the idea of splitting the data into several non-overlapping subgroups, evaluating the posterior distribution given each independent subgroup, and then combining the results. Our main contribution is the proposed aggregation step which is based on finding the geometric median of subset posterior distributions. Presented theoretical and numerical results confirm the advantages of our approach.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320